1. Field of the Invention.
This invention relates to apparatus and methods of enabling the implicit inference of missing information contained in time-dependent signals and, more particularly, to such apparatus and methods used in applications such as the terminal guidance of tactical missiles when critical signals may be denied.
2. Description of the Related Art.
An adaptive polynomial network is an ordered structure of physically realizable algebraic functions arranged in a manner that gives certain information processing properties to the overall network. Depending on the specific application, such properties may be viewed as "artificial intelligence" attributes of the constructed network.
Polynomial descriptions form the basis of the polynomial theory of complex systems. The polynomial description of a complex system or of a component is obtained by observing inputs and outputs during a comparatively short time. The central problem solved by the polynomial theory is to arrive at the polynomial description which has optimum complexity, namely, that which is sufficient to describe the complexity of the plant.
Polynomial descriptions have distinct advantages in engineering. There is no need to find solutions for the equations in finite difference form because all the relevant questions can be answered from the polynomial description itself. In polynomial theory there is no distinction made between static and dynamic states of the system being described.
Some examples of the related art are described briefly below.
U.S. Pat. No. 2,249,238 to Spang et al disclosed an improved gas turbine engine control system which maintains a selected level of engine performance despite the failure or abnormal operation of one or more engine parameter sensors. Estimate signals are calculated on-line from an analytical steady-state model of the gas turbine engine control system stored in a computer. The estimate signals are transmitted to a control computational unit which uses them in lieu of the actual engine parameter sensor signals to control the operation of the engine. The estimate signals are also compared with the corresponding actual engine parameter sensor signals and the resulting difference signals are utilized to update the engine model.
Devices such as the one described by Spang et al are well known in control literature as "model reference adaptive systems," in which a theoretical model (mathematical) of a plant is used to generate corrective control signals should the actual dynamics of the plant undergo radical changes, such as when unforeseen interruptions occur.
U.S.S.R. Patent 378802 to Ivakhnenko et al relates to a technique for modifying the input signal to a plant n the event of signal failure or plant interruption. An error signal is generated using a set of comparator and differentiator circuits.
The technique described in Soviet Patent 378802 is commonly known as a "signal-reference adaptive technique". It applies to a signal-input single-output system.
U.S.S.R. Patents S.U. 1167-618-A to Krom'er et al and S.U. 1171807-A to Anisimov et al are both mechanizations of elementary interpolation formulas with no adaptive features.
U.S. Pat. No. 4,528,565 to Hauptmann again utilizes the mechanization of simple interpolations when received discrete signal amplitudes deviate from adjacent pulse amplitudes. The particular application involved is a pulse Doppler radar receiver. U.S. Pat. No. 4,042,923 to Merrick also falls in the same category, in an application involving a radar position locator system using a mobile and at least two stationary transceivers. Signals are validated by being stored and compared with previously received signals. If the comparison indicates that the received signal is within a range of a reasonably possible change, as compared to the previously received signals, it is accepted. Otherwise, it is rejected and the memory circuit which stored the signal for validation is backed up to the last previously stored valid signal.
None of the patents described above discloses a method or apparatus in which no explicit interpolation or reconstruction is involved. None of them discloses a "trained" network which infers the effects of a continuously missing array of inputs and generates the outputs autonomously. It would be a great advancement in the art if a truly general-purpose multi-input/multi-output machine that uses no memory units, comparators, or interpolators were developed which was adaptable to diverse applications.